Construction of a poor prognosis prediction and visualization system for intracranial aneurysm endovascular intervention treatment based on an improved machine learning model - Original research

Aug 5, 2026

Improved machine learning models are evaluated for their clinical utility in predicting poor prognosis after endovascular intervention for intracranial aneurysms and for supporting decision-making through risk visualization. A retrospective cohort of 303 patients from four hospitals (January 2022 to September 2023) is analyzed, with outcomes split into good (n=207) and poor (n=96) prognosis groups. The approach integrates feature selection and weight determination, validated using multivariate logistic analysis and accompanied by an automated risk-level visualization system.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages9

Reading Time23 min

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Introduction

Methods

Results

Conclusions

TYPE Original Research PUBLISHED 08 January 2025 DOI 10. 3389/fneur.2024.1482119 OPEN ACCESS EDITED BY Luis Rafael Moscote-Salazar, Colombian Clinical Research Group in Neurocritical Care, Colombia REVIEWED BY Luis Alberto Camputaro, Specialized Institute “Hospital El Salvador”, El Salvador Linshuoshuo Lyu, Vanderbilt University Medical Center, United States Hujin Xie, Queensland University of Technology, Australia Hirokazu Koseki, Jikei University School of Medicine, Japan *CORRESPONDENCE Bo Zhou [xiayuyouqingtian@126.com](xiayuyouqingtian@126.com) RECEIVED 17 August 2024 ACCEPTED 16 December 2024 PUBLISHED 08 January 2025 CITATION Lei C, Fu A, Li B, Zhou S, Liu J, Cao Y and Zhou B (2025) Construction of a poor prognosis prediction and visualization system for intracranial aneurysm endovascular intervention treatment based on an improved machine learning model. Front. Neurol. 15:1482119 . doi: 10.3389/fneur.2024.1482119 COPYRIGHT © 2025 Lei, Fu, Li, Zhou, Liu, Cao and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Construction of a poor prognosis prediction and visualization system for intracranial aneurysm endovascular intervention treatment based on an improved machine learning model Chunyu Lei1 , Anhui Fu2 , Bin Li3 , Shengfu Zhou4 , Jun Liu3 , Yu Cao1 and Bo Zhou3* 1 Department of Neurosurgery, FuShun County Zigong City People’s Hospital, Fushun, China, 2 Department of Neurosurgery, Nanchong Central Hospital, Nanchong, China, 3 Department of Neurology, The Third People’s Hospital of Yibin, Yibin, China, 4 Department of Neurosurgery, The Sixth People’s Hospital of Yibin, Yibin, China Objective: To evaluate the clinical utility of improved machine learning models in predicting poor prognosis following endovascular intervention for intracranial aneurysms and to develop a corresponding visualization system. Methods: A total of 303 patients with intracranial aneurysms treated with endovascular intervention at four hospitals (FuShun County Zigong City People’s Hospital, Nanchong Central Hospital, The Third People’s Hospital of Yibin, The Sixth People’s Hospital of Yibin) from January 2022 to September 2023 were selected. These patients were divided into a good prognosis group (n = 207) and a poor prognosis group (n = 96) . An improved machine learning model was employed to analyze patient clinical data, aiding in the construction of a prediction model for poor prognosis in intracranial aneurysm endovascular intervention. This model simultaneously performed feature selection and weight determination. Logistic multivariate analysis was used to validate the selected features. Additionally, a visualization system was developed to automatically calculate the risk level of poor prognosis. Results: In the training set, the improved machine learning model achieved a maximum F1 score of 0 .8633 and an area under the curve (AUC) of 0 .9118. In the test set, the maximum F1 score was 0.7500, and the AUC was 0.8684. The model identiﬁed 10 key variables: age, hypertension, preoperative aneurysm rupture, Hunt-Hess grading, Fisher score, ASA grading, number of aneurysms, intraoperative use of etomidate, intubation upon leaving the operating room, and surgical time. These variables were consistent with the results of logistic multivariate analysis. Conclusions: The application of improved machine learning models for the analysis of patient clinical data can e􀀀ectively predict the risk of poor prognosis following endovascular intervention for intracranial aneurysms at an early sta

FAQs

What is the main purpose of the study?

To evaluate whether an improved machine learning model can predict poor prognosis after endovascular intervention for intracranial aneurysms and to develop a visualization system for risk calculation.

How were patients selected and grouped?

A total of 303 patients treated with endovascular intervention at four hospitals between January 2022 and September 2023 were included, and they were divided into a good prognosis group (n=207) and a poor prognosis group (n=96).

Which variables were identified as key predictors?

The model identified 10 key variables: age, hypertension, preoperative aneurysm rupture, Hunt-Hess grading, Fisher score, ASA grading, number of aneurysms, intraoperative use of etomidate, intubation upon leaving the operating room, and surgical time.

Aug 5, 2026

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